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Latent Profiles of Childhood Adversity, Adolescent Mental Health, and Neural Network Connectivity
Abstract
Importance
Adverse childhood experiences are pervasive and heterogeneous, with potential lifelong consequences for psychiatric morbidity and brain health. Existing research does not capture the complex interplay of multiple adversities, resulting in a lack of precision in understanding their associations with neural function and mental health.
Objectives
To identify distinct childhood adversity profiles and examine their associations with adolescent mental health and brain connectivity.
Design, Setting, and Participants
This population-based birth cohort used data for children who were born in 20 large US cities between 1998 and 2000 and participated in the Future Families and Child Well-Being Study. Families were interviewed when children were born and at ages 1, 3, 5, 9, and 15 years. At age 15 years, neuroimaging data were collected from a subset of these youths. Data were collected from February 1998 to April 2017. Analyses were conducted from March to December 2023.
Exposures
Latent profiles of childhood adversity, defined by family and neighborhood risks across ages 0 to 9 years.
Main Outcomes and Measures
Internalizing and externalizing symptoms at age 15 years using parent- and youth-reported measures. Profile-specific functional magnetic resonance imaging connectivity across the default mode network (DMN), salience network (SN), and frontoparietal network (FPN).
Results
Data from 4210 individuals (2211 [52.5%] male; 1959 [46.5%] Black, 1169 [27.7%] Hispanic, and 786 [18.7%] White) revealed 4 childhood adversity profiles: low-adversity (1230 individuals [29.2%]), medium-adversity (1973 [46.9%]), high-adversity (457 [10.9%]), and high maternal depression (MD; 550 [13.1%]). High-adversity, followed by MD, profiles had the highest symptoms. Notably, internalizing symptoms did not differ between these 2 profiles (mean difference, 0.11; 95% CI, −0.03 to 0.26), despite the MD profile showing adversity levels most similar to the medium-adversity profile. In the neuroimaging subsample of 167 individuals (91 [54.5%] female; 128 [76.6%] Black, 11 [6.6%] Hispanic, and 20 [12.0%] White; mean [SD] age, 15.9 [0.5] years), high-adversity and MD profiles had the highest DMN density relative to other profiles ( F (3,163) = 11.14; P < .001). The high-adversity profile had lower SN density relative to the low-adversity profile (mean difference, −0.02; 95% CI, −0.04 to −0.003) and the highest FPN density among all profiles ( F (3,163) = 18.96; P < .001). These differences were specific to brain connectivity during an emotion task, but not at rest.
Conclusions and Relevance
In this cohort study, children who experienced multiple adversities, or only elevated MD, had worse mental health and different neural connectivity in adolescence. Interventions targeting multiple risk factors, with a focus on maternal mental health, could produce the greatest benefits.
... In this context, latent class analysis has been used to identify potential clusters or configurations of ACEs based on latent or unobserved characteristics among individuals (e.g. Hardi et al., 2024;Lanier et al., 2018;Miedema et al., 2023;Shin et al., 2018). This method has allowed researchers to identify subgroups of individuals who have experienced unique patterns of ACEs rather than providing an overall index of experiences (e.g. ...
... Decades of research now demonstrate a robust relationship between ACEs and a range of issues later in life, such as criminal justice involvement, financial problems, substance use, and mental and physical health problems (e.g. Hardi et al., 2024;Hughes et al., 2017;Schilling et al., 2007). ...
... Some researchers take the approach of documenting the additive accumulation of multiple environmental risks as it relates to adolescent psychopathology 33,34 . Increasingly, though, researchers are identifying the cooccurring interplay among different environmental experiences to identify relative contributions of these environments on adolescent psychopathology 35,36 . A recent study using the ABCD Study provided evidence of four distinct profiles of perceived threat across family, school, and neighborhood systems 35 . ...
... ; https://doi.org/10.1101/2024.12.17.628982 doi: bioRxiv preprint mental health outcomes, but youth in the family threat profile uniquely showed more disruptive behavior symptoms and youth in the elevated neighborhood threat profile displayed increased sleep problems. Another study found distinct groups of youth who experienced low, medium, and high adversity, and maternal depression from family and neighborhood systems 36 . Youth reported lowest externalizing and internalizing symptoms in the low adversity followed by medium, maternal depression, and high adversity profiles. ...
Objective
Environmental factors have long been shown to influence brain structure and adolescent psychopathology. However, almost no research has included environmental factors spanning micro-to-macro-systems, brain structure, and psychopathology in an integrated framework. Here, we assessed the ways and degree to which multi-system environmental factors during late childhood predict subcortical volume and psychopathology during early adolescence.
Method
We used the baseline and 2-year follow-up data from the Adolescent Brain Cognitive Development SM Study ( N = 2,766). A Bayesian latent profile analysis was applied to obtain distinct multi-system environmental profiles during late childhood. The profiles were used in a path analysis to predict their direct and indirect effects on subcortical volume and psychopathology during early adolescence.
Results
Bayesian latent profile analysis revealed nine environmental profiles. Two distinct profiles predicted greater externalizing problems in adolescents: (i) adversity across, family, school, and neighborhood systems and (ii) family conflict and low school involvement. In contrast, a profile of family and neighborhood affluence predicted fewer externalizing difficulties. Further, family and neighborhood affluence predicted higher subcortical volume, which in turn, predicted fewer externalizing problems; whereas, family economic and neighborhood adversity predicted lower subcortical volume, which in turn, predicted greater externalizing difficulties.
Conclusion
We captured direct and indirect influences of environmental factors across multiple systems on externalizing psychopathology. Specifying the equifinal pathways to externalizing psychopathology serves to provide an evidence base for establishing different types of interventions based on the needs and risk profiles of youth.
Diversity and Inclusion Statement
The current study is part of the ongoing Adolescent Brain Cognitive Development SM Study (ABCD Study®) for which youth are recruited from elementary schools in the United States that are informed by gender, race, ethnicity, socioeconomic status, and urbanicity. The ABCD Study® aims to recruit youth longitudinally by sampling the sociodemographic makeup of the US population. Two of the authors self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One of the authors identifies as a part of an underrepresented gender group in science. The authors also are representative of the communities for which data was collected and contributed to design, analysis, and/or interpretation of the work. Finally, every effort was made to cite the work of authors from underrepresented and minoritized groups in academic research.
... The first wave of SAND (mean age 15.8) collected MRI data from a subsample of youth and parents from nearby cities (Detroit, Toledo, Chicago) (Gard et al., 2021;Goetschius et al., 2019;Hein et al., 2020). Additionally, extensive surveys, clinical interviews, discussion tasks, and biological measures (e.g., hair, saliva) were collected (Doom et al., 2022;Guzman et al., 2024;Hardi et al., 2024;Hein et al., 2020;Peckins et al., 2020). Based on the demographics of the cities sampled for SAND, participants in the neuroimaging study at age 15 identified primarily as Black (76 % as Black, 6 % as Hispanic), and 54 % reported a family income below $40, 000 (Hardi et al., 2022). ...
Enhancing the generalizability of neuroimaging studies requires actively engaging participants from under-represented communities. This paper leverages qualitative data to outline participant-driven recommendations for incorporating under-represented populations in neuroimaging protocols. Thirty-one participants, who had participated in neuroimaging research or could be eligible for one as part of an ongoing longitudinal study, engaged in semi-structured one-on-one interviews (84 % under-represented ethnic-racial identities and low-income backgrounds). Through thematic analysis, we identified nine relevant research practices from participants' reports, highlighting aspects of their experience that they appreciated and suggestions for improvement: (1) forming a diverse research team comprising members with whom participants can interact as equals; (2) increasing accessibility to research by providing transportation and flexible scheduling; (3) providing family-oriented spaces; (4) enriching the campus visits to include optional on-campus activities to connect with the University; (5) developing safe strategies to accommodate participants with tattoos during the MRI; (6) incorporating engaging and interactive tasks during neuroimaging sessions; (7) providing small gifts, such as a picture of one’s brain, in addition to financial compensation; (8) sharing research findings with the research participants; and (9) fostering long-term bidirectional relationships. The findings may be used to develop best practices for enhancing participant diversity in future neuroimaging studies.
Importance
Adverse life experiences have been proposed to contribute to diverse mental health problems through an association with corticolimbic functioning. Despite compelling evidence from animal models, findings from studies in humans have been mixed; activation likelihood estimation (ALE) meta-analyses have failed to identify a consistent association of adverse events with brain function.
Objective
To investigate the association of adversity exposure with altered brain reactivity using multilevel kernel density analyses (MKDA), a meta-analytic approach considered more robust than ALE to small sample sizes and methodological differences between studies.
Data Sources
Searches were conducted using PsycInfo, Medline, EMBASE, and Web of Science from inception through May 4, 2022. The following search term combinations were used for each database: trauma , posttraumatic stress disorder ( PTSD ), abuse , maltreatment , poverty , adversity , or stress ; and functional magnetic resonance imaging ( fMRI ) or neuroimaging; and emotion , emotion regulation , memory , memory processing , inhibitory control , executive functioning , reward , or reward processing .
Study Selection
Task-based fMRI studies within 4 domains (emotion processing, memory processing, inhibitory control, and reward processing) that included a measure of adverse life experiences and whole-brain coordinate results reported in Talairach or Montreal Neurological Institute space were included. Conference abstracts, books, reviews, meta-analyses, opinions, animal studies, articles not in English, and studies with fewer than 5 participants were excluded.
Data Extraction and Synthesis
Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline, 2 independent reviewers assessed abstracts and full-text articles for entry criteria. A third reviewer resolved conflicts and errors in data extraction. Data were pooled using a random-effects model and data analysis occurred from August to November 2022.
Main Outcomes and Measures
Peak activation x-axis (left-right), y-axis (posterior-anterior), and z-axis (inferior-superior) coordinates were extracted from all studies and submitted to MKDA meta-analyses.
Results
A total of 83 fMRI studies were included in the meta-analysis, yielding a combined sample of 5242 participants and 801 coordinates. Adversity exposure was associated with higher amygdala reactivity (familywise error rate corrected at P < .001; x-axis = 22; y-axis = −4; z-axis = −17) and lower prefrontal cortical reactivity (familywise error rate corrected at P < .001; x-axis = 10; y-axis = 60; z-axis = 10) across a range of task domains. These altered responses were only observed in studies that used adult participants and were clearest among those who had been exposed to severe threat and trauma.
Conclusions and Relevance
In this meta-analysis of fMRI studies of adversity exposure and brain function, prior adversity exposure was associated with altered adult brain reactivity to diverse challenges. These results might better identify how adversity diminishes the ability to cope with later stressors and produces enduring susceptibility to mental health problems.
Importance:
Cognitive deficits in depression have been associated with poor functional capacity, frontal neural circuit dysfunction, and worse response to conventional antidepressants. However, it is not known whether these impairments combine together to identify a specific cognitive subgroup (or "biotype") of individuals with major depressive disorder (MDD), and the extent to which these impairments mediate antidepressant outcomes.
Objective:
To undertake a systematic test of the validity of a proposed cognitive biotype of MDD across neural circuit, symptom, social occupational function, and treatment outcome modalities.
Design, setting, and participants:
This secondary analysis of a randomized clinical trial implemented data-driven clustering in findings from the International Study to Predict Optimized Treatment in Depression, a pragmatic biomarker trial in which patients with MDD were randomized in a 1:1:1 ratio to antidepressant treatment with escitalopram, sertraline, or venlafaxine extended-release and assessed at baseline and 8 weeks on multimodal outcomes between December 1, 2008, and September 30, 2013. Eligible patients were medication-free outpatients with nonpsychotic MDD in at least the moderate range, and were recruited from 17 clinical and academic practices; a subset of these patients underwent functional magnetic resonance imaging. This prespecified secondary analysis was performed between June 10, 2022, and April 21, 2023.
Main outcomes and measures:
Pretreatment and posttreatment behavioral measures of cognitive performance across 9 domains, depression symptoms assessed using 2 standard depression scales, and psychosocial function assessed using the Social and Occupational Functioning Assessment Scale and World Health Organization Quality of Life scale were analyzed. Neural circuit function engaged during a cognitive control task was measured using functional magnetic resonance imaging.
Results:
A total of 1008 patients (571 [56.6%] female; mean [SD] age, 37.8 [12.6] years) participated in the overall trial and 96 patients participated in the imaging substudy (45 [46.7%] female; mean [SD] age, 34.5 [13.5] years). Cluster analysis identified what may be referred to as a cognitive biotype of 27% of depressed patients with prominent behavioral impairment in executive function and response inhibition domains of cognitive control. This biotype was characterized by a specific profile of pretreatment depressive symptoms, worse psychosocial functioning (d = -0.25; 95% CI, -0.39 to -0.11; P < .001), and reduced activation of the cognitive control circuit (right dorsolateral prefrontal cortex: d = -0.78; 95% CI, -1.28 to -0.27; P = .003). Remission was comparatively lower in the cognitive biotype positive subgroup (73 of 188 [38.8%] vs 250 of 524 [47.7%]; P = .04) and cognitive impairments persisted regardless of symptom change (executive function: ηp2 = 0.241; P < .001; response inhibition: ηp2 = 0.750; P < .001). The extent of symptom and functional change was specifically mediated by change in cognition but not the reverse.
Conclusions and relevance:
Our findings suggest the presence of a cognitive biotype of depression with distinct neural correlates, and a functional clinical profile that responds poorly to standard antidepressants and instead may benefit from therapies specifically targeting cognitive dysfunction.
Trial registration:
ClinicalTrials.gov Identifier: NCT00693849.
Unstable and unpredictable environments are linked to risk for psychopathology, but the underlying neural mechanisms that explain how instability relate to subsequent mental health concerns remain unclear. In particular, few studies have focused on the association between instability and white matter structures despite white matter playing a crucial role for neural development. In a longitudinal sample recruited from a population-based study (N = 237), household instability (residential moves, changes in household composition, caregiver transitions in the first 5 years) was examined in association with adolescent structural network organization (network integration, segregation, and robustness of white matter connectomes; Mage = 15.87) and young adulthood anxiety and depression (six years later). Results indicate that greater instability related to greater global network efficiency, and this association remained after accounting for other types of adversity (e.g., harsh parenting, neglect, food insecurity). Moreover, instability predicted increased depressive symptoms via increased network efficiency even after controlling for previous levels of symptoms. Exploratory analyses showed that structural connectivity involving the left fronto-lateral and temporal regions were most strongly related to instability. Findings suggest that structural network efficiency relating to household instability may be a neural mechanism of risk for later depression and highlight the ways in which instability modulates neural development.
Background
Stressful events, such as the COVID‐19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population‐based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences.
Methods
Data‐driven network‐based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self‐reported at ages 15, 17, and 21 (during COVID‐19). During COVID‐19, participants reported on pandemic‐related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup–adversity interaction were tested to predict change in symptoms over time.
Results
Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (β = .138, p = .042), and this result remained after adjusting for additional covariates (β = .194, p = .023). Furthermore, there was a subgroup–adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (β = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (β = .237, p = .021).
Conclusions
A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.
Neural markers of pathophysiological processes underlying the dimension of subsyndromal-syndromal-level depression severity can provide objective, biologically informed targets for novel interventions to help prevent the onset of depressive and other affective disorders in individuals with subsyndromal symptoms, and prevent worsening symptom severity in those with these disorders. Greater functional connectivity (FC) among the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. We examined in young adults (1) relationships among activity and FC in these networks and current depression severity, using a paradigm designed to examine WM and ER capacity in n = 90, age = 21.7 (2.0); (2) the extent to which these relationships were specific to depression versus mania/hypomania; (3) whether findings in a first, “discovery” sample could be replicated in a second, independent, “test” sample of young adults n = 96, age = 21.6 (2.1); and (4) whether such relationships also predicted depression at up to 12 months post scan and/or mania/hypomania severity in (n = 61, including participants from both samples, age = 21.6 (2.1)). We also examined the extent to which there were common depression- and anxiety-related findings, given that depression and anxiety are highly comorbid. In the discovery sample, current depression severity was robustly predicted by greater activity and greater positive functional connectivity among the CEN, DMN, and SN during working memory and emotional regulation tasks (all ps < 0.05 qFDR). These findings were specific to depression, replicated in the independent sample, and predicted future depression severity. Similar neural marker–anxiety relationships were shown, with robust DMN–SN FC relationships. These data help provide objective, neural marker targets to better guide and monitor early interventions in young adults at risk for, or those with established, depressive and other affective disorders.
Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation, though this may be a dubious assumption. Alternative model specifications have been proposed to reduce nonconvergence without imposing covariance equality constraints. These methods perform well when the correct number of classes is known, but research has not yet examined their use when the number of classes is unknown. Given the importance of selecting the number of classes, more information about class enumeration performance is crucial to assess the potential utility of these methods. We conducted an extensive simulation to explore class enumeration and classification accuracy of model specifications that are more robust to nonconvergence. Results show that the typical approach of applying covariance equality constraints performs quite poorly. Instead, we recommended covariance pattern GMMs because they (a) had the highest convergence rates, (b) were most likely to identify the correct number of classes, and (c) had the highest classification accuracy in many conditions, even with modest sample sizes. An analysis of empirical posttraumatic stress disorder (PTSD) data is provided to show that the typical four-class solution found in many empirical PTSD studies may be an artifact of the covariance equality constraint method that has permeated this literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Different dimensions of adversity may affect mental health through distinct neurobiological mechanisms, though current supporting evidence consists largely of cross-sectional associations between threat or deprivation and fronto-limbic circuitry. In this exploratory three-wave longitudinal study spanning ages 9–19 years, we examined the associations between experiences of unpredictability, threat, and deprivation with the development of functional connectivity within and between three brain networks implicated in psychopathology: the salience (SAL), default mode (DMN), and fronto-parietal (FPN) networks, and tested whether network trajectories moderated associations between adversity and changes in internalizing symptoms. Connectivity decreased with age on average; these changes differed by dimension of adversity. Whereas family-level deprivation was associated with lower initial levels and more stability across most networks, unpredictability was associated with stability only in SAL connectivity, and threat was associated with stability in FPN and DMN-SAL connectivity. In youth exposed to higher levels of any adversity, lower initial levels and more stability in connectivity were related to smaller increases in internalizing symptoms. Our findings suggest that whereas deprivation is associated with widespread neurodevelopmental differences in cognitive and emotion processing networks, unpredictability is related selectively to salience detection circuitry. Studies with wider developmental windows should examine whether these neurodevelopmental alterations are adaptive or serve to maintain internalizing symptoms.
Brain structure is often studied as a marker of youth psychopathology by examining associations between volume or thickness of individual regions and specific diagnoses. However, these univariate approaches do not address whether the effect of a particular region may depend on the structure of other regions. Here, we identified subgroups of individuals with distinct profiles of brain structure and examined how these profiles were associated with concurrent and future youth psychopathology. We used latent profile analysis to identify distinct neuroanatomical profiles of subcortical region volume and orbitofrontal cortical thickness in the ABCD study (N = 9376, mean age = 9.91, SD = 0.62). We identified a five-profile solution consisting of a reduced subcortical volume profile, a reduced orbitofrontal thickness profile, a reduced limbic and elevated striatal volume profile, an elevated orbitofrontal thickness and reduced striatal volume profile, and an elevated orbitofrontal thickness and subcortical volume profile. While controlling for age, sex, and intracranial volume, profiles exhibited differences in concurrent psychopathology measured dimensionally and categorically and in psychopathology at 1-year follow-up measured dimensionally. Results show that profiles of brain structure have incremental validity for associations with youth psychopathology beyond intracranial volume.
The operationalization of childhood trauma and adversity into checklists commonly known as adverse childhood experiences, or ACEs, has become the most widely adopted methodology linking traumatic childhoods to adult outcomes. As the number of self-reported ACEs increase from 0 to 4 or more (4+), most studies find a roughly stepwise progression in risk for a wide range of negative medical and mental health outcomes. A score of 4+ ACEs, has become a de facto cutpoint, increasingly used clinically to define "high risk" status for a myriad of outcomes. Comparisons across studies using a 4+ cutpoint, however, find considerable heterogeneity in the degree of risk for the same outcomes. In addition to sample and methodological differences, certain pairs of ACEs comprising the cumulative ACE score interact synergistically to significantly increase the overall risk beyond the sum (or product) of the contributions of each ACE to the outcome. This article reviews the empirical literature on synergistic ACEs including results from a general population adult and a mixed trauma, youth sample both sufficiently powered to examine over 20 different ACE pairings for possible synergy. Synergistic pairs of ACEs vary by gender and age group. About 30-40% of the variance in outcomes is accounted for by additive synergistic interactions between certain pairs of ACEs. Across studies, sexual abuse is the most synergistically reactive ACE. The article concludes with a discussion of the implications of synergistic ACE pairings for psychologists and other allied professionals across clinical practice, prevention, research, and policy. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Background
Childhood adversity is, unfortunately, highly prevalent and strongly associated with later psychopathology. Recent theories posit that two dimensions of early adversity, threat and deprivation, have distinct effects on brain development. The current study evaluated whether violence exposure (threat) and social deprivation (deprivation) were associated with adolescent amygdala and ventral striatum activation, respectively, in a prospective, well-sampled, longitudinal cohort using a pre-registered, open science approach.
Methods
167 adolescents from the Fragile Families and Child Wellbeing Study completed fMRI scanning. Prospective longitudinal data from ages 3, 5 and 9 were used to create indices of childhood violence exposure and social deprivation. We evaluated whether these dimensions were associated with adolescent brain function in response to threatening and rewarding faces.
Results
Childhood violence exposure was associated with decreased amygdala habituation (i.e., more sustained activation) and activation to angry faces in adolescence, whereas childhood social deprivation was associated with decreased ventral striatum activation to happy faces in adolescence. These associations held when controlling for the other dimension of adversity and their interaction, gender, internalizing psychopathology, and current life stress.
Conclusions
Consistent with recent theories, different forms of early adversity were associated with region-specific differences in brain activation.
Importance:
Adverse childhood experiences are a public health issue with negative sequelae that persist throughout life. Current theories suggest that adverse childhood experiences reflect underlying dimensions (eg, violence exposure and social deprivation) with distinct neural mechanisms; however, research findings have been inconsistent, likely owing to variability in how the environment interacts with the brain.
Objective:
To examine whether dimensional exposure to childhood adversity is associated with person-specific patterns in adolescent resting-state functional connectivity (rsFC), defined as synchronized activity across brain regions when not engaged in a task.
Design, setting, and participants:
A sparse network approach in a large sample with substantial representation of understudied, underserved African American youth was used to conduct an observational, population-based longitudinal cohort study. A total of 183 adolescents aged 15 to 17 years from Detroit, Michigan; Toledo, Ohio; and Chicago, Illinois, who participated in the Fragile Families and Child Wellbeing Study were eligible for inclusion. Environmental data from birth to adolescence were collected via telephone and in-person interviews, and neuroimaging data collected at a university lab. The study was conducted from February 1, 1998, to April 26, 2017, and data analysis was performed from January 3, 2019, to May 22, 2020.
Exposures:
Composite variables representing violence exposure and social deprivation created from primary caregiver reports on children at ages 3, 5, and 9 years.
Main outcomes and measures:
Resting-state functional connectivity person-specific network metrics (data-driven subgroup membership, density, and node degree) focused on connectivity among a priori regions of interest in 2 resting-state networks (salience network and default mode) assessed with functional magnetic resonance imaging.
Results:
Of the 183 eligible adolescents, 175 individuals (98 girls [56%]) were included in the analysis; mean (SD) age was 15.88 (0.53) years and 127 participants (73%) were African American. Adolescents with high violence exposure were 3.06 times more likely (95% CI, 1.17-8.92) to be in a subgroup characterized by high heterogeneity (few shared connections) and low network density (sparsity). Childhood violence exposure, but not social deprivation, was associated with reduced rsFC density (β = -0.25; 95% CI, -0.41 to -0.05; P = .005), with fewer salience network connections (β = -0.26; 95% CI, -0.43 to -0.08; P = .005) and salience network-default mode connections (β = -0.20; 95% CI, -0.38 to -0.03; P = .02). Violence exposure was associated with node degree of right anterior insula (β = -0.29; 95% CI, -0.47 to -0.12; P = .001) and left inferior parietal lobule (β = -0.26; 95% CI, -0.44 to -0.09; P = .003).
Conclusions and relevance:
The findings of this study suggest that childhood violence exposure is associated with adolescent neural network sparsity. A community-detection algorithm, blinded to child adversity, grouped youth exposed to heightened violence based only on patterns of rsFC. The findings may have implications for understanding how dimensions of adverse childhood experiences impact individualized neural development.
Childhood adversity is heterogeneous with potentially distinct dimensions of violence exposure and social deprivation. These dimensions may differentially shape emotion-based neural circuitry, such as amygdala–PFC white matter connectivity. Amygdala–orbitofrontal cortex (OFC) white matter connectivity has been linked to regulation of the amygdala’s response to emotional stimuli. Using a preregistered analysis plan, we prospectively examined the effects of childhood exposure to two dimensions of adversity, violence exposure and social deprivation, on the adolescent amygdala–PFC white matter connectivity. We also reproduced the negative correlation between amygdala–PFC white matter connectivity and amygdala activation to threat faces. 183 15-17-year-olds were recruited from the Fragile Families and Child Wellbeing Study — a longitudinal, birth cohort, sample of predominantly low-income youth. Probabilistic tractography revealed that childhood violence exposure and social deprivation interacted to predict the probability of adolescent right hemisphere amygdala–OFC white matter connectivity. High violence exposure with high social deprivation related to less amygdala–OFC white matter connectivity. Violence exposure was not associated with white matter connectivity when social deprivation was at mean or low levels (i.e., relatively socially supportive contexts). Therefore, social deprivation may exacerbate the effects of childhood violence exposure on the development of white matter connections involved in emotion processing and regulation. Conversely, social support may buffer against them.
In precision medicine, a common question for researchers is whether patients can be classified with others who have similar risks and treatment responses. Such groupings can assist in predicting risk and matching patients with appropriate treatment strategies. The challenge is that it is often not easy to identify meaningful clusters of people with the observable data.
Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. The application of LCA is an active area of research and continues to evolve. As more researchers begin to apply the approach, detailed information on key considerations in conducting LCA is needed. In the present article, we describe LCA, review key elements to consider when conducting LCA, and provide an example of its application.
An extensive literature on childhood adversity and neurodevelopment has emerged over the past decade. We evaluate two conceptual models of adversity and neurodevelopment—the dimensional model of adversity and stress acceleration model—in a systematic review of 109 studies using MRI-based measures of neural structure and function in children and adolescents. Consistent with the dimensional model, children exposed to threat had reduced amygdala, medial prefrontal cortex (mPFC), and hippocampal volume and heightened amygdala activation to threat in a majority of studies; these patterns were not observed consistently in children exposed to deprivation. In contrast, reduced volume and altered function in frontoparietal regions were observed consistently in children exposed to deprivation but not children exposed to threat. Evidence for accelerated development in amygdala-mPFC circuits was limited but emerged in other metrics of neurodevelopment. Progress in charting neurodevelopmental consequences of adversity requires larger samples, longitudinal designs, and more precise assessments of adversity. Expected final online publication date for the Annual Review of Developmental Psychology, Volume 1 is December 16, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
The term "salience network" refers to a suite of brain regions whose cortical hubs are the anterior cingulate and ventral anterior insular (i.e. frontoinsular) cortices. This network, which also includes nodes in the amygdala, hypothalamus, ventral striatum, thalamus, and specific brainstem nuclei, co-activates in response to diverse experimental tasks and conditions, suggesting a domain-general function. In the 12 years since its initial description, the salience network has been extensively studied, using diverse methods, concepts, and mammalian species, including healthy and diseased humans across the lifespan. Despite this large and growing body of research, the essential functions of the salience network remain uncertain. In this paper, which makes no attempt to comprehensively review this literature, I describe the circumstances surrounding the initial discovery, conceptualization, and naming of the salience network, highlighting aspects that may be unfamiliar to many readers. I then discuss some of the key advances provided by subsequent research and conclude by posing a few of the questions that remain to be explored.
Background
Adverse childhood experience (ACE) scores have become a common approach for considering childhood adversities and are highly influential in public policy and clinical practice. Their use is also controversial. Other ways of measuring adversity ‐ examining single adversities, or using theoretically or empirically driven methods ‐ might have advantages over ACE scores.
Methods
In this narrative review we critique the conceptualisation and measurement of ACEs in research, clinical practice, public health and public discourse.
Results
The ACE score approach has the advantages – and limitations – of simplicity: its simplicity facilitates wide‐ranging applications in public policy, public health and clinical settings but risks over‐simplistic communication of risk/causality, determinism and stigma. The other common approach – focussing on single adversities ‐ is also limited because adversities tend to co‐occur. Researchers are using rapidly accruing datasets on ACEs to facilitate new theoretical and empirical approaches but this work is at an early stage, e.g. weighting ACEs and including severity, frequency, duration and timing. More research is needed to establish what should be included as an ACE, how individual ACEs should be weighted, how ACEs cluster, and the implications of these findings for clinical work and policy. New ways of conceptualising and measuring ACEs that incorporate this new knowledge, while maintaining some of the simplicity of the current ACE questionnaire, could be helpful for clinicians, practitioners, patients and the public.
Conclusions
Although we welcome the current focus on ACEs, a more critical view of their conceptualisation, measurement, and application to practice settings is urgently needed.
Connectivity modeling in functional neuroimaging has become widely used method of analysis for understanding functional architecture. One method for deriving directed connectivity models is Group Iterative Multiple Model Estimation (GIMME; Gates and Molenaar, 2012). GIMME looks for commonalities across the sample to detect signal from noise and arrive at edges that exist across the majority in the group (“group-level edges”) and individual-level edges. In this way, GIMME obtains generalizable results via the group-level edges while also allowing for between subject heterogeneity in connectivity, moving the field closer to obtaining reliable personalized connectivity maps. In this article, we present a novel extension of GIMME, confirmatory subgrouping GIMME, which estimates subgroup-level edges for a priori known groups (e.g. typically developing controls vs. clinical group). Detecting edges that consistently exist for individuals within predefined subgroups aids in interpretation of the heterogeneity in connectivity maps and allows for subgroup-specific inferences. We describe this algorithm, as well as several methods to examine the results. We present an empirical example that finds similarities and differences in resting state functional connectivity among four groups of children: typically developing controls (TDC), children with autism spectrum disorder (ASD), children with Inattentive (ADHD-I) and Combined (ADHD-C) Type ADHD. Findings from this study suggest common involvement of the left Broca's area in all the clinical groups, as well as several unique patterns of functional connectivity specific to a given disorder. Overall, the current approach and proof of principle findings highlight a novel and reliable tool for capturing heterogeneity in complex mental health disorders.
Background: A growing body of research identifies the harmful effects that adverse childhood experiences (ACEs; occurring during childhood or adolescence; eg, child maltreatment or exposure to domestic violence) have on health throughout life. Studies have quantified such effects for individual ACEs. However, ACEs frequently co-occur and no synthesis of findings from studies measuring the effect of multiple ACE types has been done.
Methods: In this systematic review and meta-analysis, we searched five electronic databases for cross-sectional, case-control, or cohort studies published up to May 6, 2016, reporting risks of health outcomes, consisting of substance use, sexual health, mental health, weight and physical exercise, violence, and physical health status and conditions, associated with multiple ACEs. We selected articles that presented risk estimates for individuals with at least four ACEs compared with those with none for outcomes with sufficient data for meta-analysis (at least four populations). Included studies also focused on adults aged at least 18 years with a sample size of at least 100. We excluded studies based on high-risk or clinical populations. We extracted data from published reports. We calculated pooled odds ratios (ORs) using a random-effects model.
Findings: Of 11 621 references identified by the search, 37 included studies provided risk estimates for 23 outcomes, with a total of 253 719 participants. Individuals with at least four ACEs were at increased risk of all health outcomes compared with individuals with no ACEs. Associations were weak or modest for physical inactivity, overweight or obesity, and diabetes (ORs of less than two); moderate for smoking, heavy alcohol use, poor self-rated health, cancer, heart disease, and respiratory disease (ORs of two to three), strong for sexual risk taking, mental ill health, and problematic alcohol use (ORs of more than three to six), and strongest for problematic drug use and interpersonal and self-directed violence (ORs of more than seven). We identified considerable heterogeneity (I2 of >75%) between estimates for almost half of the outcomes.
Interpretation: To have multiple ACEs is a major risk factor for many health conditions. The outcomes most strongly associated with multiple ACEs represent ACE risks for the next generation (eg, violence, mental illness, and substance use). To sustain improvements in public health requires a shift in focus to include prevention of ACEs, resilience building, and ACE-informed service provision. The Sustainable Development Goals provide a global platform to reduce ACEs and their life-course effect on health.
Funding: Public Health Wales.
Applied Latent Class Analysis introduces several innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis contributed essays to this volume, each presenting a key innovation to the basic latent class model and illustrating how it can prove useful in situations typically encountered in actual research.
The R package lcmm provides a series of functions to estimate statistical
models based on the linear mixed model theory. It includes the estimation of
mixed models and latent class mixed models for Gaussian longitudinal outcomes
(hlme), curvilinear and ordinal univariate longitudinal outcomes (lcmm) and
multivariate longitudinal outcomes (multlcmm), as well as joint latent class
mixed models (Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome
and a time-to-event that can be possibly left-truncated right-censored and
defined in a competing setting. Maximum likelihood estimators are obtained
using a modified Marquardt algorithm with strict convergence criteria based on
the first and second derivatives in addition to the parameters and likelihood
stability. The package also provides various post-fit functions including
goodness-of-fit analyses, classification, plots, predicted trajectories,
individual dynamic prediction of the event and predictive accuracy assessment.
This paper constitutes a companion paper by introducing each family of models,
the estimation technique, some implementation details and giving examples
through a dataset on cognitive aging.
Pediatric psychologists are often interested in finding patterns in heterogeneous cross-sectional data. Latent variable mixture modeling is an emerging person-centered statistical approach that models heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of this article is to offer a nontechnical introduction to cross-sectional mixture modeling.
An overview of latent variable mixture modeling is provided and 2 cross-sectional examples are reviewed and distinguished.
Step-by-step pediatric psychology examples of latent class and latent profile analyses are provided using the Early Childhood Longitudinal Study-Kindergarten Class of 1998-1999 data file.
Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar data patterns to determine the extent to which these patterns may relate to variables of interest.
Childhood multiple risk factor exposure exceeds the adverse developmental impacts of singular exposures. Multiple risk factor exposure may also explain why sociodemographic variables (e.g., poverty) can have adverse consequences. Most research on multiple risk factor exposure has relied upon cumulative risk (CR) as the measure of multiple risk. CR is constructed by dichotomizing each risk factor exposure (0 = no risk; 1 = risk) and then summing the dichotomous scores. Despite its widespread use in developmental psychology and elsewhere, CR has several shortcomings: Risk is designated arbitrarily; data on risk intensity are lost; and the index is additive, precluding the possibility of statistical interactions between risk factors. On the other hand, theoretically more compelling multiple risk metrics prove untenable because of low statistical power, extreme higher order interaction terms, low robustness, and collinearity among risk factors. CR multiple risk metrics are parsimonious, are statistically sensitive even with small samples, and make no assumptions about the relative strengths of multiple risk factors or their collinearity. CR also fits well with underlying theoretical models (e.g., Bronfenbrenner's, 1979, bioecological model; McEwen's, 1998, allostasis model of chronic stress; and Ellis, Figueredo, Brumbach, & Schlomer's, 2009, developmental evolutionary theory) concerning why multiple risk factor exposure is more harmful than singular risk exposure. We review the child CR literature, comparing CR to alternative multiple risk measurement models. We also discuss strengths and weaknesses of developmental CR research, offering analytic and theoretical suggestions to strengthen this growing area of scholarship. Finally, we highlight intervention and policy implications of CR and child development research and theory. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
As the number and scope of longitudinal investigations have expanded, so too have strategies for analyzing prospective data. Different analytic techniques are designed to answer different types of research questions. Person-centered approaches identify groups of individuals who share particular attributes or relations among attributes. They are well suited for addressing questions that concern group differences in patterns of development. Variable-centered approaches describe associations between variables. They are well suited for addressing questions that concern the relative contributions that predictor variables make to an outcome. This special issue includes conceptual essays and empirical reports designed to demonstrate the complementary strengths of these two different approaches. The articles illustrate how the integration of person-oriented and variable-oriented approaches can lead to a more complete understanding of the processes and patterns of human development. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Importance
Suicide rates in the US increased by 35.6% from 2001 to 2021. Given that most individuals die on their first attempt, earlier detection and intervention are crucial. Understanding modifiable risk factors is key to effective prevention strategies.
Objective
To identify distinct suicide profiles or classes, associated signs of suicidal intent, and patterns of modifiable risks for targeted prevention efforts.
Design, Setting, and Participants
This cross-sectional study used data from the 2003-2020 National Violent Death Reporting System Restricted Access Database for 306 800 suicide decedents. Statistical analysis was performed from July 2022 to June 2023.
Exposures
Suicide decedent profiles were determined using latent class analyses of available data on suicide circumstances, toxicology, and methods.
Main Outcomes and Measures
Disclosure of recent intent, suicide note presence, and known psychotropic usage.
Results
Among 306 800 suicide decedents (mean [SD] age, 46.3 [18.4] years; 239 627 males [78.1%] and 67 108 females [21.9%]), 5 profiles or classes were identified. The largest class, class 4 (97 175 [31.7%]), predominantly faced physical health challenges, followed by polysubstance problems in class 5 (58 803 [19.2%]), and crisis, alcohol-related, and intimate partner problems in class 3 (55 367 [18.0%]), mental health problems (class 2, 53 928 [17.6%]), and comorbid mental health and substance use disorders (class 1, 41 527 [13.5%]). Class 4 had the lowest rates of disclosing suicidal intent (13 952 [14.4%]) and leaving a suicide note (24 351 [25.1%]). Adjusting for covariates, compared with class 1, class 4 had the highest odds of not disclosing suicide intent (odds ratio [OR], 2.58; 95% CI, 2.51-2.66) and not leaving a suicide note (OR, 1.45; 95% CI, 1.41-1.49). Class 4 also had the lowest rates of all known psychiatric illnesses and psychotropic medications among all suicide profiles. Class 4 had more older adults (23 794 were aged 55-70 years [24.5%]; 20 100 aged ≥71 years [20.7%]), veterans (22 220 [22.9%]), widows (8633 [8.9%]), individuals with less than high school education (15 690 [16.1%]), and rural residents (23 966 [24.7%]).
Conclusions and Relevance
This study identified 5 distinct suicide profiles, highlighting a need for tailored prevention strategies. Improving the detection and treatment of coexisting mental health conditions, substance and alcohol use disorders, and physical illnesses is paramount. The implementation of means restriction strategies plays a vital role in reducing suicide risks across most of the profiles, reinforcing the need for a multifaceted approach to suicide prevention.
A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.
Adversity early in life is common and is a major risk factor for the onset of psychopathology. Delineating the neurodevelopmental pathways by which early adversity affects mental health is critical for early risk identification and targeted treatment approaches. A rapidly growing cross-species literature has facilitated advances in identifying the mechanisms linking adversity with psychopathology, specific dimensions of adversity and timing-related factors that differentially relate to outcomes, and protective factors that buffer against the effects of adversity. Yet, vast complexity and heterogeneity in early environments and neurodevelopmental trajectories contribute to the challenges of understanding risk and resilience in the context of early adversity. In this overview, the author highlights progress in four major areas-mechanisms, heterogeneity, developmental timing, and protective factors; synthesizes key challenges; and provides recommendations for future research that can facilitate progress in the field. Translation across species and ongoing refinement of conceptual models have strong potential to inform prevention and intervention strategies that can reduce the immense burden of psychopathology associated with early adversity.
The so-called resting state, in which participants lie quietly with no particular inputs or outputs, represented a paradigm shift from conventional task-based studies in human neuroimaging. Our foray into rest was fruitful from both a scientific and methodological perspective, but at this point, how much more can we learn from rest on its own? While rest still dominates in many subfields, data from tasks have empirically demonstrated benefits, as well as the potential to provide insights about the mind in addition to the brain. I argue that we can accelerate progress in human neuroscience by de-emphasizing rest in favor of more grounded experiments, including promising integrated designs that respect the prominence of self-generated activity while offering enhanced control and interpretability.
Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.
Few if any experts believe that existing psychiatric diagnostic categories included in DSM and ICD are actually discrete disease entities. Attention-deficit/hyperactivity disorder (ADHD) is emblematic of the problems in the existing psychiatric classification system. ADHD symptoms reliably cluster into two correlated dimensions in factor analysis. However, children with ADHD vary considerably in their symptom profiles, symptom trajectories, clinical outcomes, and biological and psychological correlates. Thus, the field has sought alternative approaches that harness the dimensions of emotional, cognitive, and behavioral functioning that underlie ADHD and other existing psychiatric categories to create informative phenotypes that improve clinical prediction and clarify etiology. Within ADHD, cognitive (neuropsychological) and temperament/personality features have received considerable attention. In some cases, subphenotypes based on these features appear to improve on existing classifications and could eventually be translated into clinical practice. This review summarizes findings from subphenotyping efforts in ADHD that use cognitive, emotion-related, and other features to highlight major considerations for research applying person-oriented approaches to inform an improved psychiatric nosology. Considerations related to feature selection, validation of newly proposed divisions, defining populations of interest, and incorporating a developmental perspective are discussed.
The present study tested how two different dimensions of childhood adversity, violence exposure and social deprivation, were associated with the cortisol response to the Socially Evaluated Cold-Pressor task in a sample of 222 adolescents (n = 117 girls, n = 167 African American). Participants were part of the Fragile Families and Child Wellbeing Study, a probability sample of births in large US cities (>200,000) between 1998 and 2000. Our subsample includes births in three cities: Detroit, Toledo, and Chicago. The study design called for an oversampling of births to unmarried parents (3:1) which led to a large number of minority and economically disadvantaged adolescents. When children were ages 3, 5, and 9, mothers reported on exposures to violence and social deprivation that occurred in the past year. Exposures from the three waves were averaged to reflect violence exposure and social deprivation during childhood. Greater levels of violence exposure from ages 3 to 9 were associated with a blunted cortisol response to stress at age 15, even after controlling for social deprivation and other factors known to influence cortisol reactivity. Social deprivation from ages 3 to 9 was not associated with the cortisol response to stress; though in an exploratory analysis, social deprivation moderated the association between violence exposure and cortisol peak activation. In line with the Dimensional Model of Adversity and Psychopathology, these findings suggest that experiences of violence, but not social deprivation, during childhood may contribute to cortisol blunting that has been previously reported in samples with high levels of social deprivation. Findings from the present longitudinal study on a relatively large sample of under-represented minority youth provide insight into the ways two different dimensions of childhood adversity impact the cortisol response to stress.
The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.
The full text is available here: https://rdcu.be/bo7o2.
Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.
Network science is booming! While the insights and images afforded by network mapping techniques are compelling, implementing the techniques is often daunting to researchers. Thus, the aim of this tutorial is to facilitate implementation in the context of GIMME, or group iterative multiple model estimation. GIMME is an automated network analysis approach for intensive longitudinal data. It creates person-specific networks that explain how variables are related in a system. The relations can signify current or future prediction that is common across people or applicable only to an individual. The tutorial begins with conceptual and mathematical descriptions of GIMME. It proceeds with a practical discussion of analysis steps, including data acquisition, preprocessing, program operation, a posteriori testing of model assumptions, and interpretation of results; throughout, a small empirical data set is analyzed to showcase the GIMME analysis pipeline. The tutorial closes with a brief overview of extensions to GIMME that may interest researchers whose questions and data sets have certain features. By the end of the tutorial, researchers will be equipped to begin analyzing the temporal dynamics of their heterogeneous time series data with GIMME.
Children who have been exposed to maltreatment and other adverse childhood experiences (ACEs) are at increased risk for various negative adult health outcomes, including cancer, liver disease, substance abuse, and depression. However, the proximal associations between ACEs and behavioral outcomes during the middle childhood years have been understudied. In addition, many of the ACE studies contain methodological limitations such as reliance on retrospective reports and limited generalizability to populations of lower socioeconomic advantage. The current study uses data from the Fragile Families and Child Wellbeing Study, a national urban birth cohort, to prospectively assess the adverse experiences and subsequent behavior problems of over 3000 children. Eight ACE categories to which a child was exposed by age 5 were investigated: childhood abuse (emotional and physical), neglect (emotional and physical), and parental domestic violence, anxiety or depression, substance abuse, or incarceration. Results from bivariate analyses indicated that Black children and children with mothers of low education were particularly likely to have been exposed to multiple ACE categories. Regression analyses showed that exposure to ACEs is strongly associated with externalizing and internalizing behaviors and likelihood of ADHD diagnosis in middle childhood. Variation in these associations by racial/ethnic, gender, and maternal education subgroups are examined. This study provides evidence that children as young as 9 begin to show behavioral problems after exposure to early childhood adversities.
It is important to estimate the burden of and trends for violence, crime, and abuse in the lives of children.
To provide health care professionals, policy makers, and parents with current estimates of exposure to violence, crime, and abuse across childhood and at different developmental stages.
The National Survey of Children's Exposure to Violence (NatSCEV) includes a representative sample of US telephone numbers from August 28, 2013, to April 30, 2014. Via telephone interviews, information was obtained on 4000 children 0 to 17 years old, with information about exposure to violence, crime, and abuse provided by youth 10 to 17 years old and by caregivers for children 0 to 9 years old.
Exposure to violence, crime, and abuse using the Juvenile Victimization Questionnaire.
In total, 37.3% of youth experienced a physical assault in the study year, and 9.3% of youth experienced an assault-related injury. Two percent of girls experienced sexual assault or sexual abuse in the study year, while the rate was 4.6% for girls 14 to 17 years old. Overall, 15.2% of children and youth experienced maltreatment by a caregiver, including 5.0% who experienced physical abuse. In total, 5.8% witnessed an assault between parents. Only 2 significant rate changes could be detected compared with the last survey in 2011, namely, declines in past-year exposure to dating violence and lifetime exposure to household theft.
Children and youth are exposed to violence, abuse, and crime in varied and extensive ways, which justifies continued monitoring and prevention efforts.
The brain's default mode network consists of discrete, bilateral and symmetrical cortical areas, in the medial and lateral parietal, medial prefrontal, and medial and lateral temporal cortices of the human, nonhuman primate, cat, and rodent brains. Its discovery was an unexpected consequence of brain-imaging studies first performed with positron emission tomography in which various novel, attention-demanding, and nonself-referential tasks were compared with quiet repose either with eyes closed or with simple visual fixation. The default mode network consistently decreases its activity when compared with activity during these relaxed nontask states. The discovery of the default mode network reignited a longstanding interest in the significance of the brain's ongoing or intrinsic activity. Presently, studies of the brain's intrinsic activity, popularly referred to as resting-state studies, have come to play a major role in studies of the human brain in health and disease. The brain's default mode network plays a central role in this work. Expected final online publication date for the Annual Review of Neuroscience Volume 38 is July 08, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
The brain is constantly bombarded by stimuli, and the relative salience of these inputs determines which are more likely to capture attention. A brain system known as the 'salience network', with key nodes in the insular cortices, has a central role in the detection of behaviourally relevant stimuli and the coordination of neural resources. Emerging evidence suggests that atypical engagement of specific subdivisions of the insula within the salience network is a feature of many neuropsychiatric disorders.
The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective-population neuroscience-that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.
A recent study shows that the fronto-parietal network (FPN), and subregions therein, alters its functional connectivity with nodes of other networks based on task goals. Moreover, FPN patterns of connectivity not only reflect engagement of specific tasks, but also serve as a code that can be transferred to facilitate learning novel tasks.
We demonstrate that, under a theorem proposed by Q.H. Vuong [Econometrica 57, No. 2, 307-333 (1989; Zbl 0701.62106)], the likelihood ratio statistic based on the Kullback-Leibler information criterion or the null hypothesis that a random sample is drawn from a k 0 -component normal mixture distribution against the alternative hypothesis that the sample is drawn from a k 1 -component normal mixture distribution is asymptotically distributed as a weighted sum of independent chi-squared random variables with one degree of freedom, under general regularity conditions. We report simulation studies of two cases where we are testing a single normal versus a two-component normal mixture and a two-component normal mixture versus a three-component normal mixture. An empirical adjustment to the likelihood ratio statistic is proposed that appears to improve the rate of convergence to the limiting distribution.
This study tested a neighborhood-level approach to what often is treated as a purely familial or within-household phenomenon-the infonmal social control of children. The data analyzed were drawn from a new, multilevel assessment of 80 neighborhoods in Chicago. The results showed that, first, informal social control can be measured reliably at the neighborhood level. Second, three dimensions of neighborhood structure-concentrated poverty, ethnicity/immigration, and residential stability-were found to explain significant amounts of variation in child social control. Third, informal social control mediated 50% of the effect of residential stability on rates of adolescent delinquency. Even afteradjustingforprior levels of crime in the neighborhood, informal social control emerged as a significant inhibitor of adolescent delinquency. The collective social control of children is an important construct that should be added to theoretical accounts and research projects that stress social regulation in families.
The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.
Diagnostic Criteria From DSM-IV, by the American Psychiatric Association, 358 pp, spiral-bound, 112.50, ISBN 0-89042-065-3, Washington, DC, American Psychiatric Association, 1994.
DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, fourth edition, was developed with a great deal of input from mental health professionals and professional organizations. In addition, there was a significant collaboration between the American Psychiatric Association (APA) and the World Health Organization, as it developed the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). As a result, DSM-IV is a great improvement over the third edition (DSM-III) and the third edition, revised (DSM-III-R).
The Task Force on DSM-IV and 13 work groups (each responsible for a section
Context Although childhood adversities (CAs) are known to be highly co-occurring, most research examines their associations with psychiatric disorders one at a time. However, recent evidence from adult studies suggests that the associations of multiple CAs with psychiatric disorders are nonadditive, arguing for the importance of multivariate analysis of multiple CAs. To our knowledge, no attempt has been made to perform a similar kind of analysis among children or adolescents.
Objective To examine the multivariate associations of 12 CAs with first onset of psychiatric disorders in a national sample of US adolescents.
Design A US national survey of adolescents (age range, 13-17 years) assessing DSM-IV anxiety, mood, behavior, and substance use disorders and CAs. The CAs include parental loss (death, divorce, and other separations), maltreatment (neglect and physical, sexual, and emotional abuse), and parental maladjustment (violence, criminality, substance abuse, and psychopathology), as well as economic adversity.
Setting Dual-frame household-school samples.
Participants In total, 6483 adolescent-parent pairs.
Main Outcome Measures Lifetime DSM-IV disorders assessed using the World Health Organization Composite International Diagnostic Interview.
Results Overall, exposure to at least 1 CA was reported by 58.3% of adolescents, among whom 59.7% reported multiple CAs. The CAs reflecting maladaptive family functioning were more strongly associated than other CAs with the onset of psychiatric disorders. The best-fitting model included terms for the type and number of CAs and distinguished between maladaptive family functioning and other CAs. The CAs predicted behavior disorders most strongly and fear disorders least strongly. The joint associations of multiple CAs were subadditive. The population-attributable risk proportions across DSM-IV disorder classes ranged from 15.7% for fear disorders to 40.7% for behavior disorders. The CAs were associated with 28.2% of all onsets of psychiatric disorders.
Conclusions Childhood adversities are common, highly co-occurring, and strongly associated with the onset of psychiatric disorders among US adolescents. The subadditive multivariate associations of CAs with the onset of psychiatric disorders have implications for targeting interventions to reduce exposure to CAs and to mitigate the harmful effects of CAs to improve population mental health.